Abstract: A first example of a method according to an embodiment of the invention comprises a method performed by a wireless device for initiating performance of Quality of Experience, QoE, measurements for a first application. The wireless device receives (402), from a base station, a first request to perform one or more measurements according to a QoE measurement configuration, wherein the request comprises an indication of the QoE measurement configuration, wherein the QoE measurement configuration is associated with a service type of a first application and at least one service subtype of the first application. The wireless device initiates (404) one or more QoE measurements according to the QoE measurement configuration; and transmits (406) a QoE measurement report comprising results of the one or more QoE measurements to the base station.

Abstract: An apparatus in a mobile communication network combines information from monitoring IP flows carrying latency sensitive content passing the apparatus and information about the application behavior and target Quality of Experience (QoE) or target connectivity characteristics such as Quality of Service (QoS) from the application to provide ongoing predictions of QoE/QoS. In some cases, the apparatus exploits a probe on a device to generate traffic for learning flow characteristics not obtained from monitoring application IP flows in the network. Embodiments disclosed herein can be used to predict quality metrics for many applications where jitter/latency is a factor affecting perceived quality, such as QoE for a human consumer or QoS for machine type communications. The embodiments are applicable to the analysis of traffic carrying conversational speech.

Abstract: Methods and related user equipment and radio network node are described in which the UE receives a measurement configuration message via the radio network node, the measurement configuration message comprising a configuration identifier associated with a measurement configuration file, and in which the UE retrieves the measurement configuration file from the radio network node using the received configuration identifier.

Abstract: Embodiments disclosed herein relate to methods and apparatus for generating video frames when there is a change in the rate of received video data. In one embodiment there is provided a method of processing video data which comprises generating a video frame using received video data, encoding said video frame into a latent vector using an encoder part of a generative model, modifying the latent vector and decoding the modified latent vector using a decoder part of the generative model to generate a new video frame in response to determining a reduction in generating the video frames using the received video data.

Abstract: A method and a scoring node for estimating a user's Quality of Experience, QoE, when a service is delivered in a media stream to the user by means of a communication network. Quality-related parameters pertaining to the service delivery are extracted, and an individual opinion score is determined for each of a set of quality models by applying each respective quality model in the set on the quality-related parameters. An aggregated opinion score is then determined as a weighted average of the individual opinion scores. The aggregated opinion score can then be used as an estimation of the user's QoE for the service delivery, e.g. when evaluating the service and/or the communication network.

Abstract: Embodiments include methods for a first radio network node (RNN) in a wireless network to configure quality of experience (QoE) measurements by user equipment (UEs). Such methods include receiving, from a measurement function in the wireless network, a QoE measurement configuration (QMC associated with one or more service types; and configuring one or more UEs with the QMC. Such methods also include sending to a second RNN in the wireless network, a first request for handover of a particular one of the UEs from a first cell served by the first RNN via a first radio access technology (RAT) to a second cell served by the second RNN via a second RAT. The first request includes the QMC. Other embodiments include complementary methods performed by a second RNN or by a UE, as well as first RNNs, second RNNs, and UEs configured to perform such methods.

Abstract: Mechanisms for predicting a multimedia session MOS are provided. The multimedia session has a video session and an audio session. The video quality and the audio quality are represented by vectors of per-time-unit scores of video quality and audio quality, respectively. The multimedia session is represented by a vector of the rebuffering start times of each rebuffering event, and a vector of the rebuffering durations of each rebuffering event. Audiovisual quality features are generated from the vectors of per-time-unit scores of video and audio quality. Buffering features are generated from the vector of rebuffering start times of each rebuffering event and the vector of rebuffering durations of each rebuffering event. A multimedia session MOS is then estimated based on the generated audiovisual quality features and the generated buffering features.

Abstract: An apparatus in a mobile communication network combines information from monitoring IP flows carrying latency sensitive content passing the apparatus and information about the application behavior and target Quality of Experience (QoE) or target connectivity characteristics such as Quality of Service (QoS) from the application to provide ongoing predictions of QoE/QoS. In some cases, the apparatus exploits a probe on a device to generate traffic for learning flow characteristics not obtained from monitoring application IP flows in the network. Embodiments disclosed herein can be used to predict quality metrics for many applications where jitter/latency is a factor affecting perceived quality, such as QoE for a human consumer or QoS for machine type communications. The embodiments are applicable to the analysis of traffic carrying conversational speech.

Abstract: Methods and related user equipment are described in which the UE receives a measurement configuration message via a radio network node, the measurement configuration message comprising location information associated with a measurement configuration file, and in which the UE retrieves the measurement configuration file from a server using the received location information.

Abstract: A method and a scoring node for estimating a user's Quality of Experience, QoE, when a service is delivered in a media stream to the user by means of a communication network. Quality-related parameters pertaining to the service delivery are extracted, and an individual opinion score is determined for each of a set of quality models by applying each respective quality model in the set on the quality-related parameters. An aggregated opinion score is then determined as a weighted average of the individual opinion scores. The aggregated opinion score can then be used as an estimation of the user's QoE for the service delivery, e.g. when evaluating the service and/or the communication network.

Abstract: It is provided a method, performed by a MOS, Mean Opinion Score, estimator, for predicting a multimedia session MOS. The multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of a video quality, an initial buffering event and rebuffering events in the video, and wherein audio quality is represented by a list of per time unit scores of audio quality. The method comprises: generating video features from the list of per time unit scores of the video quality; generating audio features from the list of per time unit scores of the audio quality; generating buffering features from the initial buffering event and rebuffering events in the video; and estimating a multimedia session MOS from the generated video features, generated audio features and generated buffering features by using machine learning technique.

Abstract: A method and a scoring node for estimating a user's Quality of Experience, QoE, when a service is delivered in a media stream to the user by means of a communication network. Quality-related parameters pertaining to the service delivery are extracted, and an individual opinion score is determined for each of a set of quality models by applying each respective quality model in the set on the quality-related parameters. An aggregated opinion score is then determined as a weighted average of the individual opinion scores. The aggregated opinion score can then be used as an estimation of the user's QoE for the service delivery, e.g. when evaluating the service and/or the communication network.

Abstract: It is provided a method, performed by a MOS, Mean Opinion Score, estimator, for predicting a multimedia session MOS. The multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of a video quality, an initial buffering event and rebuffering events in the video, and wherein audio quality is represented by a list of per time unit scores of audio quality. The method comprises: generating video features from the list of per time unit scores of the video quality; generating audio features from the list of per time unit scores of the audio quality; generating buffering features from the initial buffering event and rebuffering events in the video; and estimating a multimedia session MOS from the generated video features, generated audio features and generated buffering features by using machine learning technique.

Abstract: Methods and related user equipment and radio network node are described in which the UE receives a measurement configuration message via the radio network node, the measurement configuration message comprising a configuration identifier associated with a measurement configuration file, and in which the UE retrieves the measurement configuration file from the radio network node using the received configuration identifier.

Abstract: Mechanisms for predicting a multimedia session MOS are provided. The multimedia session has a video session and an audio session. The video quality and the audio quality are represented by vectors of per-time-unit scores of video quality and audio quality, respectively. The multimedia session is represented by a vector of the rebuffering start times of each rebuffering event, and a vector of the rebuffering durations of each rebuffering event. Audiovisual quality features are generated from the vectors of per-time-unit scores of video and audio quality. Buffering features are generated from the vector of rebuffering start times of each rebuffering event and the vector of rebuffering durations of each rebuffering event. A multimedia session MOS is then estimated based on the generated audiovisual quality features and the generated buffering features.

Abstract: A method and a scoring node for estimating a user's Quality of Experience, QoE, when a service is delivered in a media stream to the user by means of a communication network. Quality-related parameters pertaining to the service delivery are extracted, and an individual opinion score is determined for each of a set of quality models by applying each respective quality model in the set on the quality-related parameters. An aggregated opinion score is then determined as a weighted average of the individual opinion scores. The aggregated opinion score can then be used as an estimation of the user's QoE for the service delivery, e.g. when evaluating the service and/or the communication network.

Abstract: There are provided mechanisms for predicting a multimedia session MOS. The multimedia session comprises a video session and an audio session, wherein video quality is represented by a vector of per-time-unit scores of video quality and wherein audio quality is represented by is a vector of per-time-unit scores of audio quality. The multimedia session is represented by a vector of rebuffering start times of each rebuffering event, a vector of rebuffering durations of each rebuffering event, and an initial buffering duration being the time between an initiation of the multimedia session and a start time of the multimedia session. The method comprises generating audiovisual quality features from the vector of per-time-unit scores of video quality and the vector of per-time-unit scores of audio quality.

Abstract: A method and a scoring node for estimating a user's Quality of Experience, QoE, when a service is delivered in a media stream to the user by means of a communication network. Quality-related parameters pertaining to the service delivery are extracted, and an individual opinion score is determined for each of a set of quality models by applying each respective quality model in the set on the quality-related parameters. An aggregated opinion score is then determined as a weighted average of the individual opinion scores. The aggregated opinion score can then be used as an estimation of the user's QoE for the service delivery, e.g. when evaluating the service and/or the communication network.

Abstract: Methods and related user equipment are described in which the UE receives a measurement configuration message via a radio network node, the measurement configuration message comprising location information associated with a measurement configuration file, and in which the UE retrieves the measurement configuration file from a server using the received location information.

Abstract: There are provided mechanisms for predicting a multimedia session MOS. The multimedia session comprises a video session and an audio session, wherein video quality is represented by a vector of per-time-unit scores of video quality and wherein audio quality is represented by is a vector of per-time-unit scores of audio quality. The multimedia session is represented by a vector of rebuffering start times of each rebuffering event, a vector of rebuffering durations of each rebuffering event, and an initial buffering duration being the time between an initiation of the multimedia session and a start time of the multimedia session. The method comprises generating audiovisual quality features from the vector of per-time-unit scores of video quality and the vector of per-time-unit scores of audio quality.